Objectives

  • Creating a new RStudio project

  • Reading in data

  • Data manipulation

  • Visualizing data

  • Saving your work

Part 1 and 2 are adapted from the material of May Institute Beginner’s statistics in R.

1. Create a new Rstudio project

It is good practice to keep a set of related data, analyses, and text self-contained in a single folder, called the working directory. All of the scripts within this folder can then use relative paths to files that indicate where inside the project a file is located (as opposed to absolute paths, which point to where a file is on a specific computer). Working this way makes it a lot easier to move your project around on your computer and share it with others without worrying about whether or not the underlying scripts will still work.

Below, we will go through the steps for creating an “R Project” for this tutorial.

  • Under the File menu, click on New project, choose New directory, then Empty project
  • Enter a name for this new folder (or “directory”), and choose a convenient location for it. This will be your working directory for the rest of the day (e.g., datavis)
  • Click on Create project
  • Create a new R script in your working directory, calling it e.g., part1-script.R.

2. Reading in data

The file we’ll be reading in is a public protein quantification dataset (Reference: http://onlinelibrary.wiley.com/doi/10.15252/emmm.201404873/abstract) that has been summarized by each run and protein with MSstats. Its experimental design is shown below:

Experimental design

We will practice with it throughout the whole module.

CRC <- read.csv("./data/CRC_train.csv")

3. Data exploration

Let’s explore some basic properties of our dataset. Go to the RStudio Environment pane and double click the CRC entry.

class shows the type of a variable, in this case a data.frame

class(CRC)
## [1] "data.frame"

dim shows the dimension of a data.frame, which are the number of rows and the number of columns

dim(CRC)
## [1] 200  79

We can see this when inspecting the structure of a data frame with the function str():

str(CRC)

colnames is short for column names.

colnames(CRC)

head shows the first 6 rows of data. Try tail to show the last 6 rows of data.

head(CRC)

Let’s explore the type of every column/variable and a summary for the value range for every column.

summary(CRC)

Inspect the possible values for the Group column using the named and numbered column selection syntax for data frames.

unique(CRC[, 'Sub_group'])
## [1] CRC     Benign  Healthy
## Levels: Benign CRC Healthy
unique(CRC[, 79])
## [1] CRC     Benign  Healthy
## Levels: Benign CRC Healthy

We may be interested in working from here on only with data for proteins TIMP1 and SERPINA3.

CRC.twopro <- CRC[,c('TIMP1', 'SERPINA3')]

We may be also interested in only CRC data.

CRC_tumor <- CRC[CRC$Sub_group=="CRC",]

4. Visualizing data

Let’s make the scatter plot with ggplot2, one of the most popular and powerful R packages for making graphics. ggplot2 is a plotting package that makes it simple to create complex plots from data in a data frame. It provides a more programmatic interface for specifying what variables to plot, how they are displayed, and general visual properties, so we only need minimal changes if the underlying data change or if we decide to change from a bar plot to a scatterplot. This helps in creating publication quality plots with minimal amounts of adjustments and tweaking.

Fist of all, we need to load the ggplot2 package

# install.packages("ggplot2")
library(ggplot2)

ggplot() initializes a ggplot object. It can be used to declare the input data frame for a graphic and to specify the set of plot aesthetics intended to be common throughout all subsequent layers unless specifically overridden.

ggplot(data = CRC)

aesthetics (aes) include the variables to be plotted and the variables to define the presentation such as:

  • position (i.e., on the x and y axes)
  • color (“outside” color)
  • fill (“inside” color)
  • shape (of points)
  • linetype
  • size
  • alpha
ggplot(data = CRC, aes(x = SERPINA3, y = TIMP1))

Geometic Objects (geoms) are the actual marks we put on a plot, such as point, line, bar. To add a geom to the plot use + operator

ggplot(data = CRC, aes(x = SERPINA3, y = TIMP1)) +
  geom_point()

The above plot can also be made with code like this:

## Assign plot to a variable
g <- ggplot(data = CRC, aes(x = SERPINA3, y = TIMP1))

## Draw the plot
g + geom_point()

Challenge

If you want to explore the relationship between the abundance of protein AFM and AHSG, what plot will you make?

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ggplot(data = CRC) +
  geom_point(mapping = aes(x = AFM, y = AHSG))

Add Aesthetics feature to the plot: Colour and shape may work well with categorical variables, while size works well for continuous variables.

# Different color for different groups
ggplot(data = CRC, aes(x = SERPINA3, y = TIMP1, color = Sub_group)) +
  geom_point()

# Different size for different groups
ggplot(data = CRC, aes(x = SERPINA3, y = TIMP1, size = Sub_group)) +
  geom_point()
## Warning: Using size for a discrete variable is not advised.

# Different shape for different groups
ggplot(data = CRC, aes(x = SERPINA3, y = TIMP1, shape = Sub_group)) +
  geom_point()

# Different opacity value for different groups
ggplot(data = CRC, aes(x = SERPINA3, y = TIMP1, alpha = Sub_group)) +
  geom_point()

# Change the shape from solid to empty
ggplot(data = CRC, aes(x = SERPINA3, y = TIMP1, shape = Sub_group)) +
  geom_point() +
  scale_shape_discrete(solid=F)

Facetting creates tables of graphics by splitting the data into subsets and displaying the same graph for each subset

ggplot(data = CRC, aes(x = SERPINA3, y = TIMP1)) +
  geom_point() + 
  facet_grid(~ Sub_group)

# Combine Facetting and color
ggplot(data = CRC, aes(x = SERPINA3, y = TIMP1, color = Sub_group)) +
  geom_point() +
  facet_grid(~ Sub_group)

ggplot(data = na.omit(CRC_tumor), aes(x = SERPINA3, y = TIMP1, color = Gender)) +
  geom_point() +
  facet_grid(Cancer_stage ~ Tumour_location, scales = "free")

# Free axis limit
ggplot(data = na.omit(CRC_tumor), aes(x = SERPINA3, y = TIMP1, color = Gender)) +
  geom_point() +
  facet_grid(Cancer_stage ~ Tumour_location, scales = "free")

5. Saving your work

You can save plots to a number of different file formats. PDF is by far the most common format because it’s lightweight, cross-platform and scales up well but jpegs, pngs and a number of other file formats are also supported. Let’s redo the last barplot but save it to the file system this time.

Let’s save the boxplot as pdf file.

pdf("myplot1.pdf", width=4, height=4)
ggplot(data = CRC, aes(x = SERPINA3, y = TIMP1, color = Sub_group)) +
  geom_point() +
  facet_grid(~ Group)
dev.off()
## quartz_off_screen 
##                 2
## ggplot2 function
ggsave("myplot2.pdf", width = 6, height = 6)

Finally, we can save this whole session you worked so hard on!

save.image(file='Handson1.RData')

Challenge If you want to explore the relationship between the abundance of protein AFM and AHSG in cancer tissue and see the difference across different cancer stage, what plot will you make? Save your plot!

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ggplot(data = CRC_tumor) +
  geom_point(mapping = aes(x = AFM, y = AHSG,color = as.factor(Cancer_stage)))

ggsave("my-plot.pdf", width = 6, height = 6)

6. R markdown

Markdown (extension .md) is a very simple markup language in plain text, that can be converted to many different outputs, such as pdf and html to name a few.

It is also possible to interleave R code into markdown documents, producing so called R mardown documents (extension .Rmd). Before generating the final html or pdf output, the Rmd files are first knit, i.e. the R code chunks are extracted, exectuted and replaced by their output, be it text, tables or figures.

Rmarkdown is a fantastic tool to implement reproducible research, as we have the guarantee that code results and figures, displayed in the final document, accurately represent the analysis restults. They provide a relatively easly way to generate high quality and reproducible analysis reports for any arbitrary input data that is submitted to an established analysis routine. The R package that supports this is rmarkdown, and the whole process is made relatively easy with RStudio and available documentation.

We are going to walk through the creation of such a document.